Pandemics, Public Health, and the Regulation of Borders
Bibliographic record
Abstract
This book examines how the COVID-19 pandemic has engendered a new and challenging environment in which borders drawn around people, places, and social structures have hardened and new ones have emerged. Over the course of the COVID-19 pandemic, borders closed or became unwelcoming at the international, national, sub-national, and local levels. Debate persists as to whether those countries and territories that tightly managed their borders, like New Zealand, Australia, or Hong Kong, got it ‘right’ compared to those that did not. Without doubt, a majority of those who suffered and died throughout the pandemic have been those from vulnerable populations. Yet on the other hand, efforts taken to manage the spread of the disease, such as through border management, have also disproportionately affected those who are most vulnerable. How then is the right balance to be struck, acknowledging, too, the economic and other imperatives that may dissuade governments from taking public health steps? This book considers how international organizations, countries, and institutions within those countries should conceive of, and manage, borders as the world continues to struggle with COVID-19 and prepares for the next pandemic. Engaging a range of international, and sub-national, examples, the book thematizes the main issues at stake in the control and management of borders in the interests of public health. This book will be of considerable interest to academics in the fields of health law, anthropology, economics, history, medicine, public health, and political science, as well as policymakers and public health planners at national and sub-national levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".